Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

Vector Regression”, which is most frequently used in "case classification".
The concept here is to discover a line in space that divides data points into
distinct categories. Its also used for regression analysis. It is a form of
"binary classification" technique that is not associated with probability.
"Ridge Regression" is a widely used method for analyzing multi-collinear
data set. Depending on the features of the data set, using ridge regression
correctly can decrease standard errors and significantly improve model
accuracy.
Ridge regression can be helpful if your data includes highly correlated
independent variables. If you can predict an independent variable with the
use of another independent variable, your model will exhibit a high risk of
"multi-collinearity". For example, if you use variables that measure the
height and weight of a person; these variables in the model are likely to
create "multi-collinearity".
Multicollinearity could potentially influence the accuracy of the forecasts
and predictions generated by the model. Be mindful of the type of
"predictive variables" being utilized in the model to prevent
multicollinearity, which could be caused by the type of data you are using,
as well as the data collection method. Another reason could be the selection
of a small variety of independent variables or the selection of independent
variables was very restricted, which resulted in very similar data points.

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Multicollinearity can also be induced by generating a highly specific model.
Tor that there are more variables than data points in the model. If you have
selected 
to 
utilize 
a 
"linear 
model" 
which 
ended
up worsening multicollinearity of the model, then you can attempt to
implement a method of "ridge regression".
Ridge regression operates to render the predictions more accurate by
permitting a hint of bias into the model. This technique is also referred to as
"regularization".
Another technique to enhance the accuracy of the model is by
"standardizing" the independent variables. The easiest route is to decrease
complexity by changing the values of certain independent variables to null.
The approach is not simply to modify these independent variables to null
but to implement a structure that rewards values closer to zero. This will
trigger the coefficients to decrease, so the model's complexity is also
reduced, but the model maintains all of its independent variables. This will
offer the model more bias, which is a trade-off for increased accuracy
of predictions.
Another technique of reduction is called "LASSO regression". A very
complementary 
technique 
to 
the 
"ridge 
regression", 
"lasso
regression" promotes the use of simpler and leaner models to
generate predictions. In lasso regression, the model reduces the value of
coefficients relatively more rigidly. LASSO stands for the "least absolute
shrinkage and selection operator". Data on our scatterplot, like the mean
or median values of the data are reduced to a more compact level. We use
this when the model is experiencing high multicollinearity similar to
the "ridge regression" model.

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A hybrid of "LASSO" and "ridge regression" methods is known as